feat(portfolio): add CARA, convex, mean-variance & ERC portfolio optimization module
New Rust portfolio_optimization module with PyO3 bindings:
- CARA/CRRA utility maximization via projected gradient descent
- General-purpose convex objective solver on simplex (ProjectedGradientSolver)
- Mean-variance optimization (max Sharpe, target return, min variance)
- Equal Risk Contribution (ERC) portfolio allocation
- Python bindings: cara_optimal_weights, mean_variance_optimal_weights,
min_variance_weights, erc_weights
- 6/6 unit tests passing
Convergence fix: removed gradient-norm criterion on simplex boundary
(projected gradient never vanishes at constrained optimum).
Default learning rate increased from 0.005 to 0.1.
ThotDjehuty
e67b0f8376